OpenRaiser/Intern-Atlas huggingface dataset:
Euclid's Gift: Enhancing Spatial Perception and Reasoning in Vision-Language Models via Geometric Surrogate Tasks
Venue: arXiv cs.CV 2025
Year: 2025

Spatial intelligence spans a rich suite of abilities, including visualising and transforming shapes, mentally rotating objects, judging relational positions and containment, and estimating numerosity. However, it still remains a critical unresolved challenge for Multimodal Large Language Models (MLLMs). To fill this gap, we propose to treat Euclidean geometry problem-solving as a surrogate task. Specifically, we meticulously constructed a curated multimodal dataset, called Euclid30K, comprising approximately 30K plane and solid geometry problems. Furthermore, to enable the model to learn and apply Euclidean principles from these geometry problems, we fine-tuned seven model variants (spanning 3--72B parameters) from the Qwen2.5VL, Qwen3VL, and RoboBrain2.0 families using Group Relative Policy Optimization (GRPO), inspiring the models to identify shapes, count, and relate entities, and perform multi-step deductive reasoning using Euclidean principles. Our experiments demonstrate that the resulting models achieve substantial zero-shot gains across four spatial reasoning benchmarks (Super-CLEVR, Omni3DBench, VSI-Bench, and MindCube) without any task-specific adaptations. Notably, after training on the Euclid30K, the mean VSI-Bench accuracy rose from 36.6\% to 41.8\% (+5.2\%), and the mean MindCube accuracy rose from 31.4\% to 38.1\% (+6.7\%). To our knowledge, this is the first systematic study showing that geometry-centric fine-tuning can confer vision-language models with broadly transferable spatial skills. Code and Euclid30K dataset can be found in \href{https://zgca-ai4edu.github.io/Euclids_Gift}{this}.
Guided Uncertainty Learning Using a Post-Hoc Evidential Meta-Model
Venue: arXiv cs.LG 2025
Year: 2025

Reliable uncertainty quantification remains a major obstacle to the deployment of deep learning models under distributional shift. Existing post-hoc approaches that retrofit pretrained models either inherit misplaced confidence or merely reshape predictions, without teaching the model when to be uncertain. We introduce GUIDE, a lightweight evidential learning meta-model approach that attaches to a frozen deep learning model and explicitly learns how and when to be uncertain. GUIDE identifies salient internal features via a calibration stage, and then employs these features to construct a noise-driven curriculum that teaches the model how and when to express uncertainty. GUIDE requires no retraining, no architectural modifications, and no manual intermediate-layer selection to the base deep learning model, thus ensuring broad applicability and minimal user intervention. The resulting model avoids distilling overconfidence from the base model, improves out-of-distribution detection by ~77% and adversarial attack detection by ~80%, while preserving in-distribution performance. Across diverse benchmarks, GUIDE consistently outperforms state-of-the-art approaches, evidencing the need for actively guiding uncertainty to close the gap between predictive confidence and reliability.
JSProtect: A Scalable Obfuscation Framework for Mini-Games in WeChat
Venue: arXiv cs.SE 2025
Year: 2025

The WeChat mini-game ecosystem faces rampant intellectual property theft to other platforms via secondary development, yet existing JavaScript obfuscation tools are ill-equipped for large-scale applications, suffering from prohibitive processing times, severe runtime performance degradation, and unsustainable code size inflation. This paper introduces JSProtect, a high-throughput parallelized obfuscation framework designed to overcome these fundamental limitations. At the core of our framework is the Parallel-Aware Scope Analysis (PASA) algorithm, which enables two key optimizations: independent code partitioning for multi-core processing and independent namespace management that aggressively reuses short identifiers to combat code bloat. Our evaluation demonstrates that JSProtectprocesses 20MB codebases in minutes, maintaining 100\% semantic equivalence while controlling code size inflation to as low as 20\% compared to over 1,000\% with baseline tools. Furthermore, it preserves near-native runtime performance and provides superior security effectiveness against both static analysis tools and large language models. This work presents a new paradigm for industrial-scale JavaScript protection that effectively balances robust security with high performance and scalability.
Experience-Guided Reflective Co-Evolution of Prompts and Heuristics for Automatic Algorithm Design
Venue: arXiv cs.AI 2025
Year: 2025

Combinatorial optimization problems are traditionally tackled with handcrafted heuristic algorithms, which demand extensive domain expertise and significant implementation effort. Recent progress has highlighted the potential of automatic heuristics design powered by large language models (LLMs), enabling the automatic generation and refinement of heuristics. These approaches typically maintain a population of heuristics and employ LLMs as mutation operators to evolve them across generations. While effective, such methods often risk stagnating in local optima. To address this issue, we propose the Experience-Guided Reflective Co-Evolution of Prompt and Heuristics (EvoPH) for automatic algorithm design, a novel framework that integrates the island migration model with the elites selection algorithm to simulate diverse heuristics populations. In EvoPH, prompts are co-evolved with heuristic algorithms, guided by performance feedback. We evaluate our framework on two problems, i.e., Traveling Salesman Problem and Bin Packing Problem. Experimental results demonstrate that EvoPH achieves the lowest relative error against optimal solutions across both datasets, advancing the field of automatic algorithm design with LLMs.
Trading Carbon for Physics: On the Resource Efficiency of Machine Learning for Spatio-Temporal Forecasting
Venue: arXiv cs.LG 2025
Year: 2025

Development of modern deep learning methods has been driven primarily by the push for improving model efficacy (accuracy metrics). This sole focus on efficacy has steered development of large-scale models that require massive resources, and results in considerable carbon footprint across the model life-cycle. In this work, we explore how physics inductive biases can offer useful trade-offs between model efficacy and model efficiency (compute, energy, and carbon). We study a variety of models for spatio-temporal forecasting, a task governed by physical laws and well-suited for exploring different levels of physics inductive bias. We show that embedding physics inductive biases into the model design can yield substantial efficiency gains while retaining or even improving efficacy for the tasks under consideration. In addition to using standard physics-informed spatio-temporal models, we demonstrate the usefulness of more recent models like flow matching as a general purpose method for spatio-temporal forecasting. Our experiments show that incorporating physics inductive biases offer a principled way to improve the efficiency and reduce the carbon footprint of machine learning models. We argue that model efficiency, along with model efficacy, should become a core consideration driving machine learning model development and deployment.
Deep Reinforcement Learning in Action: Real-Time Control of Vortex-Induced Vibrations
Venue: arXiv cs.LG 2025
Year: 2025

This study showcases an experimental deployment of deep reinforcement learning (DRL) for active flow control (AFC) of vortex-induced vibrations (VIV) in a circular cylinder at a high Reynolds number (Re = 3000) using rotary actuation. Departing from prior work that relied on low-Reynolds-number numerical simulations, this research demonstrates real-time control in a challenging experimental setting, successfully addressing practical constraints such as actuator delay. When the learning algorithm is provided with state feedback alone (displacement and velocity of the oscillating cylinder), the DRL agent learns a low-frequency rotary control strategy that achieves up to 80% vibration suppression which leverages the traditional lock-on phenomenon. While this level of suppression is significant, it remains below the performance achieved using high-frequency rotary actuation. The reduction in performance is attributed to actuation delays and can be mitigated by augmenting the learning algorithm with past control actions. This enables the agent to learn a high-frequency rotary control strategy that effectively modifies vortex shedding and achieves over 95% vibration attenuation. These results demonstrate the adaptability of DRL for AFC in real-world experiments and its ability to overcome instrumental limitations such as actuation lag.
SAIP: A Plug-and-Play Scale-adaptive Module in Diffusion-based Inverse Problems
Venue: arXiv cs.LG 2025
Year: 2025

Solving inverse problems with diffusion models has shown promise in tasks such as image restoration. A common approach is to formulate the problem in a Bayesian framework and sample from the posterior by combining the prior score with the likelihood score. Since the likelihood term is often intractable, estimators like DPS, DMPS, and $π$GDM are widely adopted. However, these methods rely on a fixed, manually tuned scale to balance prior and likelihood contributions. Such a static design is suboptimal, as the ideal balance varies across timesteps and tasks, limiting performance and generalization. To address this issue, we propose SAIP, a plug-and-play module that adaptively refines the scale at each timestep without retraining or altering the diffusion backbone. SAIP integrates seamlessly into existing samplers and consistently improves reconstruction quality across diverse image restoration tasks, including challenging scenarios.
UniDex: Rethinking Search Inverted Indexing with Unified Semantic Modeling
Venue: arXiv cs.IR 2025
Year: 2025

Inverted indexing has traditionally been a cornerstone of modern search systems, leveraging exact term matches to determine relevance between queries and documents. However, this term-based approach often emphasizes surface-level token overlap, limiting the system's generalization capabilities and retrieval effectiveness. To address these challenges, we propose UniDex, a novel model-based method that employs unified semantic modeling to revolutionize inverted indexing. UniDex replaces complex manual designs with a streamlined architecture, enhancing semantic generalization while reducing maintenance overhead. Our approach involves two key components: UniTouch, which maps queries and documents into semantic IDs for improved retrieval, and UniRank, which employs semantic matching to rank results effectively. Through large-scale industrial datasets and real-world online traffic assessments, we demonstrate that UniDex significantly improves retrieval capabilities, marking a paradigm shift from term-based to model-based indexing. Our deployment within Kuaishou's short-video search systems further validates UniDex's practical effectiveness, serving hundreds of millions of active users efficiently.
Identity Bridge: Enabling Implicit Reasoning via Shared Latent Memory
Venue: arXiv cs.LG 2025
Year: 2025

Despite remarkable advances, large language models often fail at compositional reasoning tasks, a phenomenon exemplified by the ``curse of two-hop reasoning''. This paper introduces the Identity Bridge, a simple yet powerful mechanism that resolves this compositionality gap by supervising the model on a zero-hop identity task. We demonstrate empirically that this addition enables models to successfully perform out-of-distribution two-hop reasoning, a task they otherwise completely fail. To explain this phenomenon, we provide a theoretical analysis using a simplified Emb-MLP model, proving that identity supervision reshapes the model's latent geometry. We show this alignment is induced by an implicit nuclear-norm regularization during optimization, which favors low-rank solutions that share structure across tasks. For complex tasks, we use small initialization or weight decay to enhance the regularization effect, which enhances the latent space alignment effect and slows down the generalization decay. Finally, we extend our investigation to large-scale models, observing that they still achieve two-hop reasoning through the latent memory, which provides crucial inspiration for enhancing their implicit reasoning abilities.
VNODE: A Piecewise Continuous Volterra Neural Network
Venue: arXiv cs.CV 2025
Year: 2025

This paper introduces Volterra Neural Ordinary Differential Equations (VNODE), a piecewise continuous Volterra Neural Network that integrates nonlinear Volterra filtering with continuous time neural ordinary differential equations for image classification. Drawing inspiration from the visual cortex, where discrete event processing is interleaved with continuous integration, VNODE alternates between discrete Volterra feature extraction and ODE driven state evolution. This hybrid formulation captures complex patterns while requiring substantially fewer parameters than conventional deep architectures. VNODE consistently outperforms state of the art models with improved computational complexity as exemplified on benchmark datasets like CIFAR10 and Imagenet1K.
Data-Driven Discrete Geofence Design Using Binary Quadratic Programming
Venue: arXiv cs.SI 2025
Year: 2025

Geofences have attracted significant attention in the design of spatial and virtual regions for managing and engaging spatiotemporal events. By using geofences to monitor human activity across their boundaries, content providers can create spatially triggered events that include notifications about points of interest within a geofence by pushing spatial information to the devices of users. Traditionally, geofences were hand-crafted by providers. In addition to the hand-crafted approach, recent advances in collecting human mobility data through mobile devices can accelerate the automatic and data-driven design of geofences, also known as the geofence design problem. Previous approaches assume circular shapes; thus, their flexibility is insufficient, and they can only handle geofence-based applications for large areas with coarse resolutions. A challenge with using circular geofences in urban and high-resolution areas is that they often overlap and fail to align with political district boundaries and road segments, such as one-way streets and median barriers. In this study, we address the problem of extracting arbitrary shapes as geofences from human mobility data to mitigate this problem. In our formulation, we cast the existing optimization problems for circular geofences to 0-1 integer programming problems to represent arbitrary shapes. Although 0-1 integer programming problems are computationally hard, formulating them as quadratic (unconstrained) binary optimization problems enables efficient approximation of optimal solutions, because this allows the use of specialized quadratic solvers, such as the quantum annealing, and other state-of-the-art algorithms. We then develop and compare different formulation methods to extract discrete geofences. We confirmed that our new modeling approach enables flexible geofence design.
Traumatic Brain Injury Segmentation using an Ensemble of Encoder-decoder Models
Venue: arXiv cs.CV 2025
Year: 2025

The identification and segmentation of moderate-severe traumatic brain injury (TBI) lesions pose a significant challenge in neuroimaging. This difficulty arises from the extreme heterogeneity of these lesions, which vary in size, number, and laterality, thereby complicating downstream image processing tasks such as image registration and brain parcellation, reducing the analytical accuracy. Thus, developing methods for highly accurate segmentation of TBI lesions is essential for reliable neuroimaging analysis. This study aims to develop an effective automated segmentation pipeline to automatically detect and segment TBI lesions in T1-weighted MRI scans. We evaluate multiple approaches to achieve accurate segmentation of the TBI lesions. The core of our pipeline leverages various architectures within the nnUNet framework for initial segmentation, complemented by post-processing strategies to enhance evaluation metrics. Our final submission to the challenge achieved an accuracy of 0.8451, Dice score values of 0.4711 and 0.8514 for images with and without visible lesions, respectively, with an overall Dice score of 0.5973, ranking among the top-6 methods in the AIMS-TBI 2025 challenge. The Python implementation of our pipeline is publicly available.
On the Performance of Amplitude-Based Models for Low-Rank Matrix Recovery
Venue: arXiv cs.IT 2025
Year: 2025

In this paper, we focus on low-rank phase retrieval, which aims to reconstruct a matrix $\mathbf{X}_0\in \mathbb{R}^{n\times m}$ with ${\mathrm{ rank}}(\mathbf{X}_0)\le r$ from noise-corrupted amplitude measurements $\mathbf{y}=|\mathcal{A}(\mathbf{X}_0)|+\boldsymbolη$, where $\mathcal{A}:\mathbb{R}^{n\times m}\rightarrow \mathbb{R}^{p}$ is a linear map and $\boldsymbolη\in \mathbb{R}^p$ is the noise vector. We first examine the rank-constrained nonlinear least-squares model $\hat{\mathbf{X}}\in \mathop{\mathrm{argmin}}\limits_{\substack{\mathbf{X}\in \mathbb{R}^{n\times m},\mathrm{rank}(\mathbf{X})\le r}}\||\mathcal{A}(\mathbf{X})|-\mathbf{y}\|_2^2$ to estimate $\mathbf{X}_0$, and demonstrate that the reconstruction error satisfies $\min\{\|\hat{\mathbf{X}}-\mathbf{X}_0\|_F, \|\hat{\mathbf{X}}+\mathbf{X}_0\|_F\}\lesssim \frac{\|\boldsymbolη\|_2}{\sqrt{p}}$ with high probability, provided $\mathcal{A}$ is a Gaussian measurement ensemble and $p\gtrsim (m+n)r$. We also prove that the error bound $\frac{\|\boldsymbolη\|_2}{\sqrt{p}}$ is tight up to a constant. Furthermore, we relax the rank constraint to a nuclear-norm constraint. Hence, we propose the Lasso model for low-rank phase retrieval, i.e., the constrained nuclear-norm model and the unconstrained version. We also establish comparable theoretical guarantees for these models. To achieve this, we introduce a strong restricted isometry property (SRIP) for the linear map $\mathcal{A}$, analogous to the strong RIP in phase retrieval. This work provides a unified treatment that extends existing results in both phase retrieval and low-rank matrix recovery from rank-one measurements.
Enhancing Physical Plausibility in Video Generation by Reasoning the Implausibility
Venue: arXiv cs.CV 2025
Year: 2025

Diffusion models can generate realistic videos, but existing methods rely on implicitly learning physical reasoning from large-scale text-video datasets, which is costly, difficult to scale, and still prone to producing implausible motions that violate fundamental physical laws. We introduce a training-free framework that improves physical plausibility at inference time by explicitly reasoning about implausibility and guiding the generation away from it. Specifically, we employ a lightweight physics-aware reasoning pipeline to construct counterfactual prompts that deliberately encode physics-violating behaviors. Then, we propose a novel Synchronized Decoupled Guidance (SDG) strategy, which leverages these prompts through synchronized directional normalization to counteract lagged suppression and trajectory-decoupled denoising to mitigate cumulative trajectory bias, ensuring that implausible content is suppressed immediately and consistently throughout denoising. Experiments across different physical domains show that our approach substantially enhances physical fidelity while maintaining photorealism, despite requiring no additional training. Ablation studies confirm the complementary effectiveness of both the physics-aware reasoning component and SDG. In particular, the aforementioned two designs of SDG are also individually validated to contribute critically to the suppression of implausible content and the overall gains in physical plausibility. This establishes a new and plug-and-play physics-aware paradigm for video generation.
On the Self-awareness of Large Reasoning Models' Capability Boundaries
Venue: arXiv cs.AI 2025
Year: 2025

Large Reasoning Models (LRMs) have shown impressive performance on complex reasoning tasks such as mathematics, yet they also display misbehaviors that expose their limitations. In particular, when faced with hard questions, LRMs often engage in unproductive reasoning until context limit, producing wrong answers while wasting substantial computation. This phenomenon reflects a fundamental issue: current answering paradigms overlook the relationship between questions and LRMs' capability boundaries. In this paper, we investigate whether LRMs possess self-awareness of capability boundaries. We begin by an observation that LRMs may know what they cannot solve through expressed reasoning confidence. For black-box models, we find that reasoning expressions reveal boundary signals, with accelerated growing confidence trajectory for solvable problems but convergent uncertainty trajectory for unsolvable ones. For white-box models, we show that hidden states of the last input token encode boundary information, with solvable and unsolvable problems linearly separable even before reasoning begins. Building on these findings, we propose two simple yet effective optimization strategies: reasoning expression monitoring and hidden states monitoring. Experiments demonstrate that these boundary-aware strategies enable LRMs to avoid unproductive reasoning without sacrificing accuracy, significantly improving reliability and efficiency by cutting token usage up to 62.7 - 93.6%.
ProxyAttn: Guided Sparse Attention via Representative Heads
Venue: arXiv cs.CL 2025
Year: 2025

The quadratic complexity of attention mechanisms limits the efficiency of Large Language Models (LLMs) on long-text tasks. Recently, methods that dynamically estimate block importance have enabled efficient block sparse attention, leading to significant acceleration in long-text pre-filling of LLMs. However, their coarse-grained estimation inevitably leads to performance degradation at high sparsity rates. In this work, we propose ProxyAttn, a training-free sparse attention algorithm that achieves more precise block estimation by compressing the dimension of attention heads. Based on our observation of the similarity among multiple attention heads, we use the scores of pooled representative heads to approximate the scores for all heads. To account for the varying sparsity among heads, we also propose a block-aware dynamic budget estimation method. By combining the scores from representative proxy heads with multi-head dynamic budgets, we achieve a more fine-grained block importance evaluation at low computational cost. Experiments on a variety of mainstream models and extensive benchmarks confirm the underlying similarity among attention heads. Leveraging a fine-grained estimation, the proposed method achieves substantial gains in performance and efficiency compared to existing methods. More precisely, ProxyAttn can achieve up to 10.3x attention acceleration and 2.4x prefilling acceleration without significant performance loss. Our code is available at https://github.com/wyxstriker/ProxyAttn.